Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentscope-ai/OpenJudge --skill 07-redteamgit clone --depth 1 https://github.com/agentscope-ai/OpenJudgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentscope-ai/openjudge/07-redteam)<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/07-redteam"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/07-redteam/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentscope-ai/openjudge/07-redteam"><img src="https://agentmods.dev/badge/skills/agentscope-ai/openjudge/07-redteam.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 35 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00089 | $0.03306 |
| Opus 5 | $0.00044 | $0.01653 |
| Sonnet 5 | $0.00018 | $0.00661 |
| Haiku 4.5 | $0.00009 | $0.00331 |
Grade A, and why
redteam scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Redteam
Test your application's safety boundaries systematically. This skill generates attack prompts from a policy document, measures what gets through, and pairs the Attack Success Rate (ASR) with the Over-Refusal Rate so you don't reward models that simply refuse everything.
When to Activate
- Pre-deployment safety audit
- Regulatory compliance check
- After major model or prompt changes that could affect safety
- User reports a jailbreak or injection vulnerability
- Periodic (quarterly) safety re-evaluation
Checklist
You MUST create a task for each item and complete them in order:
- Establish policy — what's prohibited, what's allowed, what's gray
- Derive attack vectors from policy — not a fixed template
- Generate attack set + over-refusal set — paired measurement
- Run attacks and evaluate — ASR per policy category
- Compute over-refusal rate — paired with ASR
- Output safety scorecard + audit document
Fast path: run the bundled script
Once attacks and benign prompts have been run against the system and judged, compute the
scorecard with the bundled, tested script (scripts/asr_report.py, standard library only,
no OpenJudge dependency) — it always pairs ASR with over-refusal so you can't reward a
system that just refuses everything:
python scripts/asr_report.py --attacks attacks.jsonl --over-refusals over.jsonl
# 1-5 harmfulness grader (5=safe): python scripts/asr_report.py --attacks attacks.jsonl --score-scale 5 --harm-threshold 3
Attack rows: {"category","vector","violated":bool} (or "score" + thresholds). Over-refusal
rows: {"category","refused":bool}. It reports ASR per category/vector and over-refusal per
category, and warns if the over-refusal set is missing. --self-test to verify it.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 346 lines · 89 tokens per session scan A d1ff8b9faeb3
redteam is a skill published in the GitHub repository agentscope-ai/OpenJudge (824 stars, last pushed 2d ago), licensed Apache-2.0. It adds 89 tokens to every session and 3,306 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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